Methods and apparatus to identify co-relationships between media using social media

ABSTRACT

Methods, apparatus, systems and articles of manufacture are disclosed to identify co-relationships between media using social media. An example apparatus includes an audience estimator to: estimate a first audience of first media based on a first set of media-exposure social media messages corresponding to client devices referencing the first media, and estimate a second audience of second media based on a second set of media-exposure social media messages corresponding to the client devices referencing the second media. The example apparatus also includes a pairing classifier to: determine a pairing-score for a media-pairing based on the first and second audiences and the first and second sets of media-exposure social media messages, determine a relationship threshold to apply to the media-pairing based on the first media and the second media, and classify the media-pairing based on the pairing-score and the relationship threshold to improve an accuracy of a system associated with generating audience analysis information.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 14/880,594, which was filed on Oct. 12, 2015. U.S. patentapplication Ser. No. 14/880,594 is hereby incorporated herein byreference in its entirety. Priority to U.S. patent application Ser. No.14/880,594 is hereby claimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience measurement, and, moreparticularly, to methods and apparatus to identify co-relationshipsbetween media using social media.

BACKGROUND

Audience measurement of media (e.g., any type of content and/oradvertisements such as broadcast television and/or radio, stored audioand/or video played back from a memory such as a digital video recorderor a digital video disc, a webpage, audio and/or video presented (e.g.,streamed) via the Internet, a video game, etc.) often involvescollection of media identifying information (e.g., signature(s),fingerprint(s), code(s), tuned channel identification information, timeof exposure information, etc.). Such audience measurement effortstypically also involve the collection of people data (e.g., useridentifier(s), demographic data associated with audience member(s),etc.). The media identifying information and the people data can becombined to generate, for example, media exposure data indicative ofamount(s) and/or type(s) of people that were exposed to specificpiece(s) of media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an example environment in which a systemconstructed in accordance with the teachings of this disclosure operatesto identify co-relationships between media using social media.

FIG. 2 is an example block diagram showing additional detail of oneexample of the example environment of FIG. 1.

FIG. 3 is an example data table that may be stored by the example socialmedia server of FIGS. 1 and/or 2 to store social media messages.

FIG. 4 is an example data table that may be stored by the examplecentral facility of FIGS. 1 and/or 2 representing media time ranges andmedia keywords associated with different media.

FIG. 5 is an example data table representing query responses that may besupplied to the example central facility of FIGS. 1 and/or 2 by theexample social media server of FIGS. 1 and/or 2.

FIG. 6 is an example data table that may be stored by the examplecentral facility of FIGS. 1 and/or 2 to store media audience estimatesbased on media identified in social media messages of interest.

FIG. 7 is an example data table that may be stored by the examplecentral facility of FIGS. 1 and/or 2 representing relationshipclassifications for media-pairings.

FIG. 8 is a flowchart representative of example machine-readableinstructions that may be executed by the example social media server ofFIGS. 1 and/or 2 to reply to a request for social media messagesassociated with media of interest.

FIG. 9 is a flowchart representative of example machine-readableinstructions that may be executed by the example social media server ofFIGS. 1 and/or 2 to reply to a request for impression informationrelated to social media messages of interest.

FIG. 10 is a flowchart representative of example machine-readableinstructions that may be executed by the example central facility ofFIGS. 1 and/or 2 to identify co-relationships between media using socialmedia.

FIG. 11 is a flowchart representative of example machine-readableinstructions that may be executed by the example central facility ofFIGS. 1 and/or 2 to identify media-exposure social media messages ofinterest.

FIG. 12 is a flowchart representative of example machine-readableinstructions that may be executed by the example central facility ofFIGS. 1 and/or 2 to estimate audiences for media based on media-exposuresocial media messages of interest.

FIG. 13 is a flowchart representative of example machine-readableinstructions that may be executed by the example central facility ofFIGS. 1 and/or 2 to classify identified media-pairings.

FIG. 14 is a block diagram of an example processing platform capable ofexecuting the example machine-readable instructions of FIGS. 8 and/or 9to implement the example social media server of FIGS. 1 and/or 2.

FIG. 15 is a block diagram of an example processing platform capable ofexecuting the example machine-readable instructions of FIGS. 10-12and/or 13 to implement the example central facility of FIGS. 1 and/or 2.

Wherever possible, the same reference numbers will be used throughoutthe drawing(s) and accompanying written description to refer to the sameor like parts.

DETAILED DESCRIPTION

Example methods, systems and apparatus disclosed herein may be used toidentify media commonly viewed by people having similar interests. Insome examples, social media messages posted by users (e.g., messageauthors) are analyzed to identify media combinations. For example,techniques disclosed herein enable utilizing social media to optimizeexposure to sponsored media by discovering and/or classifyingrelationships between media based on social media messages related tothe media.

Monitoring impressions of media (e.g., television (TV) programs, radioprograms, advertisements, commentary, audio, video, movies, commercials,websites, etc.) is useful for generating ratings or other statistics forpresented media. As used herein, an impression is defined to be an eventin which a home or individual is exposed to media (e.g., anadvertisement, content, a group of advertisements and/or a collection ofcontent). A quantity of impressions or impression count, with respect tomedia, is the total number of times homes or individuals have beenexposed to the media. For example, in audience metering systems, mediaidentifying information may be detected at one or more monitoring siteswhen the media is presented (e.g., played at monitored environments suchas households). In such examples, the collected media identifyinginformation may be sent to a central data collection facility associatedwith an audience measurement entity (AME), such as The Nielsen Company(US), LLC with people meter data identifying person(s) in the audiencefor analysis such as the computation of an impression count for themedia. That is, the audience measurement entity does not provide media(e.g., content and/or advertisements) to end users. This un-involvementwith the media production and/or delivery ensures the neutral status ofthe audience measurement entity and, thus, enhances the trusted natureof the data the AME collects and processes. The reports generated by theaudience measurement entity may identify aspects of media usage such ashow many impressions the media received. In some instances, to ensurethat the reports generated by the audience measurement entity are usefulto the media providers, it is advantageous to be able to associate mediaimpressions with demographic information. For example, the reports mayindicate a number of impressions of media grouped by demographic groupsfor a time period.

Companies and/or individuals want to understand the reach andeffectiveness of the media that they produce and/or sponsor (e.g.,through advertisements). In some examples, media that is associated witha larger number of impressions may be considered more effective atinfluencing user behavior because such media is seen by a larger numberof people than some media with a fewer number of impressions.

Audience measurement entities (sometimes referred to herein as “ratingsentities”) traditionally determine media reach and frequency bymonitoring registered panel members. That is, an audience measuremententity enrolls people who consent to being monitored into a panel. Insuch panelist-based systems, demographic information is obtained from apanelist when, for example, the panelist joins and/or registers for thepanel. The demographic information (e.g., race, age or age range,gender, marital status, income, home location, education level, etc.)may be obtained from the panelist, for example, via a telephoneinterview, an in-person interview, by having the panelist complete asurvey (e.g., an on-line survey), etc. In some examples, demographicinformation may be collected for a home (e.g., via a survey requestinginformation about members of the home). However, such panelist systemsmay be costly to implement at a scale appropriate for accuratelyidentifying and/or estimating the number of exposures of media.Moreover, in view of the increasingly large amount of media distributionchannels and media exposure possibilities, collecting a meaningfulamount of panelist information (e.g., a statistically significant samplesize) for each available media may not be practical.

Social messaging has become a widely used medium in which usersdisseminate and receive information. Online social messaging services(such as Twitter®, Facebook®, etc.) enable users to send social mediamessages or instant messages to many users at once. Some socialmessaging services enable users to “follow” or “friend” other users(e.g., subscribe to receive messages sent by select users (e.g., via theTwitter® service), status updates (e.g., via the Facebook® service orGoogle+™ social service), etc.). For example, a user following (e.g.,subscribed to, online friends with, etc.) a celebrity using the Twitter®service may receive indications via a client application (e.g., theTweetDeck® client application or any other social media messaging clientapplication) when the celebrity sends or posts a social media message.

Social media messages (sometimes referred to herein as “messages,”“statuses,” “texts” or “tweets”) may be used to convey many differenttypes of information. In some examples, social media messages are usedto relay general information about a user. For example, a message sendermay send a social media message indicating that they are bored. In someexamples, social media messages are used to convey information regardingmedia, a media event, a product and/or a service. For example, a messagesender may convey (e.g., self-report) a social media message indicatingthat the message sender is watching a certain television program,listening to a certain song, or just purchased a certain book. Socialmedia messages may include different types of media such as, forexample, images, moving images, video, etc. and/or text such as, forexample, words, abbreviations, acronyms, hashtags, alphanumeric strings,etc. Media-exposure social media messages are social media messages thatare disseminated to a mass audience and indicate exposure of at leastone media to the sender of the message. In some examples disclosedherein, social media messages are collected and then filtered toidentify media-exposure social media messages.

As used herein, a media-pairing is a combination of particular mediapresentations. In some examples, the media-pairing may include mediawith similar characteristics (e.g., genre). For example, a firstmedia-pairing may include a broadcast of a golf tournament and abroadcast of a football game and a second media-pairing may include afirst broadcast of a sitcom on a first network and a second broadcast ofthe sitcom on a second network at a different time than the firstbroadcast of the sitcom. In some examples, the media-pairing may includemedia with seemingly little similarity (e.g., cross-genre media). Forexample, a media-pairing may include a broadcast of a golf tournamentand a broadcast of a daytime soap opera.

Examples disclosed herein use social media to identify relatedmedia-pairings. For example, a first set of social media messagesregarding (e.g., referencing, mentioning, corresponding to, etc.) firstmedia (e.g., a television program) are identified. In some disclosedexamples, the first media is selected based on constraints provided fora media campaign. For example, the first media may be associated with aproduct such as golfing equipment based on, for example, a similarcategory as the product (e.g., a golfing event, a sports show, etc.),similar demographic information as the product (e.g., viewers of thefirst media may be similar to consumers of the product based on age,gender, marital status, income, geographic location, etc.), etc.Examples disclosed herein identify a second set of social media messagesregarding second media. In some disclosed examples, the media is mediathat is a one-time event such as the Super Bowl, an awards ceremony,etc.

Examples disclosed herein estimate audiences for respective media usingmessage information corresponding to identified social media messages.In some examples, audiences are estimated based on identified authors ofsocial media messages. For example, disclosed examples may accessmessage information identifying users who posted media-exposure socialmedia messages referencing media (e.g., message authors) and estimate anaudience for the media based on the identified authors. In somedisclosed examples, the audiences are estimated based on users whoaccess social media messages. For example, disclosed examples may obtainimpression information for media-exposure social media messagesreferencing media and estimate an audience for the media based onidentified users who were presented with the media-exposure social mediamessages.

In some examples, when determining the size of an audience, examplesdisclosed herein filter alias usernames and/or duplicate entriesassociated with a user. For example, a user may provide a first username(e.g., “Jon_Doe”) to register an account with a first social mediaservice and the user may provide a second username (e.g., “J_Doe_1”) toregister an account with a second social media service. In theillustrated example, if the user posts a first social media messagereferencing a hockey game while accessing the first social media serviceand posts second and third social media messages referencing the hockeywhile accessing the second social media service, examples disclosedherein may associate the three message postings with the same user andcredit the media with one audience member. Furthermore, if the user ispresented with a fourth social media message referencing the hockey game(e.g., while accessing the first social media service or the secondsocial media service), examples disclosed herein associate the messageaccess with the same user the user who posted the first three messagesand the media is not credited with an additional audience member.

Examples disclosed herein classify media-pairings based on a subset ofthe estimated audiences. For example, examples disclosed herein identifya subset of the identified audience members who are overlapping audiencemembers (e.g., audience members included in both audiences) such asuser(s) who posted one or more social media message(s) and/or who werepresented with one or more social media message(s) related to firstmedia and to second media. In some disclosed examples, to classify amedia-pairing, the number of overlapping audience members is comparedwith a threshold (e.g., 70%). For example, when an overlap ratio(sometimes referred to as an “intersection score” or a “pairing score”)satisfies a relationship threshold (e.g., the number of overlappingaudience members and total number of unique audience members for thefirst media or the second media meets and/or exceeds the relationshipthreshold), then the corresponding media is classified as related media.When the overlap ratio (e.g., the intersection score) fails to satisfythe relationship threshold (e.g., the number of overlapping audiencemembers and total number of unique audience members for the first mediaor the second media fails to meet and/or exceed the relationshipthreshold), then the corresponding media is classified as unrelatedmedia.

In some disclosed examples, the relationship threshold may depend oncharacteristics of the media and/or the media-pairing. For example, whenfirst media is relatively expensive to air, media that is a one-timeevent and/or media that is viewed by a varying demographic (e.g.,viewers of the Super Bowl include males and females, viewers between theages of 9 and 60, married viewers and single viewers, etc.), therelationship threshold may be less than, for example, a relationshipthreshold value associated with on-going media (e.g., a televisionprogram having episodes, a serial, etc.).

In some disclosed examples, the relationship threshold may increase. Forexample, when the media-pairing includes media with a planned cross-over(e.g., a character from a first television program was advertised to beappearing in a second television program), the relationship thresholdapplied to the media-pairing may increase. Additionally oralternatively, when the media-pairing includes media associated withsimilar interests (e.g., a first media-pairing including mediaassociated with cooking, a second media-pairing including mediaassociated with football, etc.), the relationship threshold applied tothe media-pairing may increase.

In some examples, the relationship threshold applied to a media-pairingmay vary based on positive or negative influences of guests (e.g.,celebrities, politicians, athletes, etc.). For example, a user who isnot interested in a television program (e.g., a talk show) may watch thetelevision program when a guest having a positive influence on the user(e.g., a football player) is appearing on the television program. Forexample, the relationship threshold applied to a media-pairing includingthe broadcast of the talk show with the football player and a broadcastof a football game may be increased to account for the expected increasein viewership in the talk show. Additionally or alternative, a user whois interested in the television program (e.g., the talk show) may avoidwatching the television program when a guest having a negative influenceon the user (e.g., a politician) is appearing on the television program.For example, the relationship threshold applied to a media-pairingincluding the broadcast of the talk show with the politician and anotherpolitical-themed television program may be decreased to account for theexpected decrease in viewership of the talk show. In some examples, therelationship threshold may be adjusted during broadcast of a televisionprogram. For example, the relationship threshold applied to a broadcastof the talk show and a broadcast of a football game may increase whilethe football player is on the talk show and then the relationshipthreshold may decrease when the politician is on the talk show.

Examples disclosed herein may utilize the related media-pairings toidentify techniques for improving marketing for media such as therespective media and/or sponsored media. For example, examples disclosedherein may process the related media-pairings and improve marketing forproducts via exposure to cross-promotional media. For example, analysisof a broadcast of a golf tournament and a broadcast of a daytime soapopera may indicate that 71% of audience members who watched thebroadcast of the golf tournament also watched the broadcast of thedaytime soap opera. In the illustrated example, it may be beneficial toproprietors of golfing equipment to broadcast sponsored media related totheir golfing equipment during future broadcasts of golf events andfuture broadcasts of the daytime soap opera.

Although examples disclosed herein are described in connection with twopieces of media (e.g., a “media-pairing”), disclosed techniques mayadditionally or alternatively be used in connection with any number ofmedia (e.g., a “media-grouping”), such as three pieces of media, fourpieces of media, etc. For example, disclosed examples may be used toidentify a unique audience who was exposed to three different programs,etc.

As used herein, a media-exposure social media message is a social mediamessage that references at least one media asset (e.g., media and/or amedia event). As used herein, a media-exposure social media message ofinterest is a media-exposure social media message that (1) is posted toa social media site contemporaneously and/or near contemporaneously witha time-window of presentation of the corresponding media asset, and (2)is accessed (e.g., viewed) at the social media site contemporaneouslyand/or near contemporaneously with the time-window of presentation ofthe corresponding media asset. Media-exposure social media messages willtypically include a message that conveys a sentiment about the mediaasset. For example, a media-exposure social media message may includethe text “Jon Stewart is really funny on The Daily Show right now!”

FIG. 1 is a diagram of an example environment in which a system 100constructed in accordance with the teachings of this disclosure operatesto co-relationships between media using social media. The example system100 of FIG. 1 includes a social media server 105 and a central facility110 operated by an audience measurement entity (AME) 115. The exampleAME 115 of the illustrated example of FIG. 1 is an entity such as TheNielsen Company (US), LLC that monitors and/or reports exposure to mediaand operates as a neutral third party. The example AME 115 of FIG. 1operates the central facility 110 to identify media-pairings in socialmedia and to classify the identified media-pairings as, for example,related media-pairings or unrelated media-pairings.

The social media server 105 provides social media services to users 120of the social media server 105. As used herein, the term social mediaservices is defined to be a service provided to users to enable users toshare information (e.g., text, images, data, etc.) in a virtualcommunity and/or network. Example social media services may include, forexample, Internet forums (e.g., a message board), blogs, micro-blogs(e.g., Twitter), social networks (e.g., Facebook, LinkedIn, Instagram,etc.), etc.

The example users 120 of the illustrated example of FIG. 1 are users ofa social media service provided by the social media server 105. In theillustrated example, the example users 120 access the social mediaservice using a media device 125 such as a mobile device (e.g., acellular phone, a smartphone, a tablet, a phablet, a smart watch, etc.).However, the social media server 105 may be accessed in any otherfashion such as, for example, using a laptop, a smart television, anin-vehicle infotainment system, etc. The example social media server 105of the illustrated example of FIG. 1 records social media messagesposted by users of the social media service and a timestamp(s)associated with the posting of the message(s).

The example social media server 105 of the illustrated example of FIG. 1receives one or more queries from the AME 115 via a network 130. Theexample network 130 of the illustrated example of FIG. 1 is theInternet. However, the example network 130 may be implemented using anysuitable wired and/or wireless network(s) including, for example, one ormore data buses, one or more Local Area Networks (LANs), one or morewireless LANs, one or more cellular networks, one or more privatenetworks, one or more public networks, etc. The example network 130enables the central facility 110 to be in communication with the socialmedia server 105. As used herein, the phrase “in communication,”including variances thereof, encompasses direct communication and/orindirect communication through one or more intermediary components anddoes not require direct physical (e.g., wired) communication and/orconstant communication, but rather includes selective communication atperiodic or aperiodic intervals, as well as one-time events.

The central facility 110 transmits the queries to gather social mediamessages of interest at periodic intervals (e.g., every 24 hours, everyMonday, etc.). In the illustrated example, the one or more queriesindicate media keywords, user identifiers and/or message identifiers.The media keywords are keywords that are associated with particularmedia (e.g., a television program) and result in social media messagesthat are relevant to the particular media being identified.

The example central facility 110 of the illustrated example of FIG. 1inspects the social media messages returned by the social media server105 for media mentioned in the social media messages. For example, thecentral facility 110 may identify the “The Daily Show” as mediareferenced in a media-exposure social media message including the text“Jon Stewart is really funny on The Daily Show right now!” In theillustrated example, when the central facility 110 identifies mediareferenced in a social media message, the social media message is amedia-exposure social media message. The example central facility 110stores information used for identifying media-exposure social mediamessages, user identifiers regarding the media-exposure social mediamessages and/or media in the media-exposure social media messages in adatabase.

As described below, the example central facility 110 of the illustratedexample of FIG. 1 estimates the audiences of respective media based onidentified media-exposure social media messages of interest. Forexample, the central facility 110 may estimate the audience for atelevision program by identifying unique authors of media-exposuresocial media messages related to the television program. In someexamples, the central facility 110 may estimate the audience for atelevision program by identifying unique users who were presentedmedia-exposure social media messages related to the television program.

The example central facility 110 of the illustrated example of FIG. 1determines an overlap ratio for a media-pairing based on the audiencesof the respective media. For example, the central facility 110 maydetermine the overlap ratio based on the number of unique audiencemembers identified in two different sets of media-exposure social mediamessages related to two different pieces of media and the total numberof unique audience members identified in each set of media-exposuresocial media messages. In some examples, the overlap ratio representsthe number of unique authors who posted media-exposure social mediamessages of interest. In some examples, the overlap ratio represents thenumber of unique users who were presented with media-exposure socialmedia messages of interest.

The example central facility 110 of the illustrated example of FIG. 1classifies media-pairings based on a comparison of the overlap ratiocalculated for the corresponding media and a relationship threshold. Forexample, when an overlap ratio satisfies the relationship threshold, thecentral facility 110 classifies the corresponding media-pairing arelated media-pairing, and when the overlap ratio does not satisfy therelationship threshold, the central facility 110 classifies themedia-pairing an unrelated media-pairing.

FIG. 2 is a block diagram of an example environment in which the system100 of FIG. 1 constructed in accordance with the teachings of thisdisclosure operates to identify co-relationships between media-pairingsusing social media. The example system 100 of FIG. 2 includes the socialmedia server 105 and the central facility 110 operated by the audiencemeasurement entity (AME) 115 of FIG. 1. The social media server 105provides social media services to users 120 of the social media server105. The example social media server 105 of FIG. 2 includes a socialmessage interface 205, a social message database 210, a social messageexposure database 215, a messages identifier 220, and a query handler225.

The example social media server 105 receives queries from the centralfacility 110 via the example network 130. The example network 130 of theillustrated example of FIG. 2 is the Internet. However, the examplenetwork 130 may be implemented using any suitable wired and/or wirelessnetwork(s) including, for example, one or more data buses, one or moreLocal Area Networks (LANs), one or more wireless LANs, one or morecellular networks, one or more private networks, one or more publicnetworks, etc. The central facility 110 transmits the queries to gathersocial media messages of interest.

The example users 120 of the illustrated example of FIG. 2 are users ofa social media service provided by the social media server 105. In theillustrated example, the user(s) 120 signs into an online social mediaservice with a user identifier to read and/or convey (e.g., post, send,etc.) social media messages. Users may then follow other users (e.g.,subscribe to messages of the other users) using the user identifier(s).

The example social message interface 205 of the illustrated example ofFIG. 2 enables users to post social media messages. For example, whenthe user 120 operates their media device 125, the social messageinterface 205 receives a social media message composed by the user 120.In examples disclosed herein, the social message interface 205 recordssocial media messages that were received for posting in the socialmessage database 210.

In some examples, the social message interface 205 also enables users toreceive social media messages posted to the social message service. Forexample, when the user 120 operates their media device 125, the socialmessage interface 205 receives a request for social media messages to bepresented to the user. The social message interface 205 gathers messagesto be transmitted to the requesting user 120. In the illustratedexample, the social message interface 205 gathers messages that wereposted by users to whom the requesting user 120 has subscribed. However,in some examples, the requesting user may have requested social mediamessages that were posted by a user other than a user to whom therequesting user has subscribed.

In examples disclosed herein, the example social message interface 205records when an impression of a social media message occurs in thesocial message exposure database 215. In the illustrated example,impressions are recorded when the social media message is transmitted tothe user. However, in some examples, the impressions are recorded when areceipt message is received from the media device indicating that thesocial media message was presented to the user.

The example social message database 210 of the illustrated example ofFIG. 2 records social media messages posted by users of the social mediaservice and a timestamp(s) associated with the posting of themessage(s). In some examples, a username (e.g., a “handle,” a screenname, a unique serial identifier, etc.) of the user posting the socialmedia message (e.g., a messages author) is stored. Storing the usernameof the posting user enables identification that the social media messageshould be presented to a user who is subscribed to receive messageposted by the author. An example data table 300 of the illustratedexample of FIG. 3 illustrates example data that may be stored in theexample social message database 210. The example social message database210 may be implemented by a volatile memory (e.g., a Synchronous DynamicRandom Access Memory (SDRAM), Dynamic Random Access Memory (DRAM),RAMBUS Dynamic Random Access Memory (RDRAM), etc.) and/or a non-volatilememory (e.g., flash memory). The example social message database 210 mayadditionally or alternatively be implemented by one or more double datarate (DDR) memories, such as DDR, DDR2, DDR3, mobile DDR (mDDR), etc.The example social message database 210 may additionally oralternatively be implemented by one or more mass storage devices such ashard disk drive(s), compact disk drive(s), digital versatile diskdrive(s), etc. While in the illustrated example the social messagedatabase 210 is illustrated as a single database, the social messagedatabase 210 may be implemented by any number and/or type(s) ofdatabases.

The example social message exposure database 215 of the illustratedexample of FIG. 2 stores social media message exposure information. Inexamples disclosed herein, the social media message information includesa message identifier, a timestamp indicating the time of exposure and auser identifier to whom the social media message was presented. Theexample social message exposure database 215 is useful because itenables identification of users who were presented with a particularmessage of interest during a given time range. The example socialmessage exposure database 215 may be implemented by a volatile memory(e.g., a Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM), etc.) and/or a non-volatile memory (e.g., flash memory). Theexample social message exposure database 215 may additionally oralternatively be implemented by one or more double data rate (DDR)memories, such as DDR, DDR2, DDR3, mobile DDR (mDDR), etc. The examplesocial message exposure database 215 may additionally or alternativelybe implemented by one or more mass storage devices such as hard diskdrive(s), compact disk drive(s), digital versatile disk drive(s), etc.While in the illustrated example the social message exposure database215 is illustrated as a single database, the social message exposuredatabase 215 may be implemented by any number and/or type(s) ofdatabases.

The example messages identifier 220 of the illustrated example of FIG. 2gathers social media message exposure information identifying the listof users that were presented with a media-exposure social media messagethat meets the criteria of a request received from the AME 115 via theexample query handler 225. The example messages identifier 220 inspectsthe example social message database 210 and/or the example socialmessage exposure database 215 to identify social media message(s) and alist of users associated with those identified social media messagesthat include keywords of interest and where the exposure occurred duringa first time period (e.g., within a particular five minute interval of alive television presentation).

In some examples, the exposure messages identifier 220 further limitsresults to social media messages that were presented during a secondtime period (e.g., social media messages that were posted ten minutes orless before the airing of the live television presentation, social mediamessages that were posted during the airing of the live televisionpresentation, etc.). In some examples, the first time period and thesecond time period are the same (e.g., resulting in identification ofsocial media messages that were both posted and viewed in the same timeperiod). In the illustrated example of FIG. 2, the example messagesidentifier 220 operates SQL queries against the example social messagedatabase 210 and/or the example social message exposure database 215 toidentify social media message(s) and a list of users associated withthose identified social media messages. However, any other approach toaccessing the data may additionally or alternatively be used.

The example query handler 225 of the illustrated example of FIG. 2receives one or more queries from the AME 115 via the network 130. Inthe illustrated example, the one or more queries indicate mediakeywords, time periods and/or message identifiers. The media keywordsare keywords that are associated with particular media (e.g., atelevision show) and result in social media messages that are relevantto the particular media being identified. In examples disclosed herein,the media keywords are selected by the AME 115 to closely correspond tomedia of interest. For example, the media keywords may include a titleof the media, an episode name, a name of a character in the media, aname of an actor, a phrase used in the media, etc.

In the illustrated example, the one or more queries received from theAME 115 indicate a time period of social media message exposure ofinterest. In the illustrated example, the time period is represented bya start time and a stop time. However, the time period may berepresented in any other fashion such as, for example, a start time andduration. Moreover, in some examples, the query may request that theresults be divided into smaller intervals (e.g., return results for a 30minute television show broken down by five minute intervals).

In some examples, the social media server 105 provides a list ofusernames (e.g., a “handle,” a screen name, a unique serial identifier,etc.) to the AME 115. For example, the AME 115 may want to identify theone or more users 120 who were presented with a particularmedia-exposure social media message of interest. In the illustratedexample, the one or more queries received from the AME 115 may indicatea particular media-exposure social media message of interest. Forexample, each social media message that is recorded in the examplesocial message database 210 may be assigned a message identifier thatmay be used to identify a particular social media message. An importantaspect of any monitoring system is maintaining the privacy of thoseusers who are monitored in accordance with their wishes. To that end,the social media server 105 does not provide user names identifyingusers who were presented with a social media message. Instead, theexample messages identifier 220 obfuscates the user identifierinformation. In some examples, the messages identifier 220 obfuscatesthe user identifier information in a manner so that the same obfuscateduser identifier information corresponds to the same user. In thismanner, user activities may be monitored for particular users withoutexposing sensitive information regarding the user. However, any otherapproach to protecting the privacy of a user may additionally oralternatively be used.

The example query handler 225 interacts with the example messagesidentifier 220 to gather results to transmit to the AME 115 in responseto the query received from the AME 115. As such, the example queryhandler 225 provides a unified interface from which the central facility110 can request social media messages and/or impression information. Anexample data table 500 representing example results returned to the AME115 by the example query handler 225 is shown in the illustrated exampleof FIG. 5.

The example AME 115 of the illustrated example of FIG. 2 is an entitysuch as The Nielsen Company (US), LLC that monitors and/or reportsexposure to media and operates as a neutral third party. That is, theaudience measurement entity does not produce, provide and/or sponsormedia to end users. This un-involvement with the media proprietors(e.g., the companies and/or individuals that produce, provide and/orsponsor media) ensures the neutral status of the audience measuremententity and, thus, enhances the trusted nature of the data it collects.The reports generated by the AME 115 may identify co-relationshipsbetween media-pairings using social media and uses for relatedmedia-pairings.

The example AME 115 operates the central facility 110 to monitor and/orreport the exposure to media. The example central facility 110 of theillustrated example of FIG. 2 may include one or more servers operatedby the AME 115. Although only one central facility 110 is shown in FIG.2, many facilities may be provided for collecting the data. In someexamples, these data collection facilities are structured in a tieredapproach with many satellite collection facilities collecting data andforwarding the same to one or more central facilities 110. The examplecentral facility 110 includes a media profile database 250, a queryengine 255, a message logger 260, a media messages database 265, anaudience estimator 270, an audience database 275, a pairing classifier280, a media-pairings database 285 and a reporter 290.

The example media profile database 250 of the illustrated example ofFIG. 2 stores media profile information such as a media identifier, amedia time range and media keywords. The media profile informationstored by the media profile database 250 enables the example queryengine 255 to generate a query to transmit to the social media server105. An example data table 400 representing the example media profileinformation is represented in the illustrated example of FIG. 4. Theexample media profile database 250 may be implemented by a volatilememory (e.g., a Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM), etc.) and/or a non-volatile memory (e.g., flash memory). Theexample media profile database 250 may additionally or alternatively beimplemented by one or more double data rate (DDR) memories, such as DDR,DDR2, DDR3, mobile DDR (mDDR), etc. The example media profile database250 may additionally or alternatively be implemented by one or more massstorage devices such as hard disk drive(s), compact disk drive(s),digital versatile disk drive(s), etc. While in the illustrated examplethe media profile database 250 is illustrated as a single database, themedia profile database 250 may be implemented by any number and/ortype(s) of databases.

The example query engine 255 of the illustrated example of FIG. 2generates queries to be transmitted to the social media server 105. Inthe illustrated example, the query engine 255 reads media profiles fromthe media profile database 250 and generates a query (or queries)requesting some or all social media messages containing media keywords(from the media profiles) and that were posted during a particular timewindow (as indicated by the media profile). Based on the times at whichvarious media is presented, the example query engine 255 determineswhich queries should be generated. For example, queries related to atelevision show that airs on Thursday evening need not be transmitted toidentify exposure to social media messages referencing the televisionshow on a Monday morning, as exposure to social media messages on Mondaymorning is not likely to have an effect on the audience on Thursdayevening.

In some examples, in addition to and/or as an alternative to generatinga query based on media keywords, the example query engine 255 generatesqueries requesting impression information associated media-exposuresocial media messages of interest. For example, the AME 115 may want toidentify the users who were presented media-exposure social mediamessages of interest (e.g., social media messages referencing a golftournament, etc.). In some such examples, the query engine 255 maygenerate a query (or queries) requesting message impression informationincluding a list of usernames by providing message identifierscorresponding to the media-exposure social media messages of interest.However, any other query criteria may additionally or alternatively beused for preparing queries.

In examples disclosed herein, the example query engine 255 generatesqueries to be transmitted at periodic intervals (e.g., every 24 hours,every Monday, etc.). However, any other time period may additionally oralternatively be used for preparing queries. For example, the queryengine 255 may generate queries at aperiodic intervals (e.g., whenrequested) and/or as a one-time event. Additionally or alternatively,instead of generating queries as they are to be transmitted, the examplequery engine 255 may generate queries ahead of time and cache thepre-generated queries.

The example query engine 255 of the illustrated example of FIG. 2transmits the queries to the query handler 225. In the illustratedexample, the queries are transmitted via the network 130 as the resultsof those queries are needed for analysis (e.g., at the end of a timeperiod). For example, a query may request social media message(s) and/ormessage impression information relevant to a particular piece of mediaand/or a particular media-exposure social media message of interest.However, in some examples, the queries may be transmitted to the queryhandler 225 ahead of time, and results may be returned upon expirationof the time period. In the illustrated example, the queries and theirassociated results are transmitted via the network 130. However, thequeries and their associated results may be transmitted in any otherfashion. In response to the query, the example query engine 255 receivesinformation associated therewith relevant to the particular social mediamessages of interest (e.g., a message identifier, the time when themessage was posted, the content of the message, impression information,etc.) and/or a list of usernames. An example data table 500 representingresponses to queries is shown in the illustrated example of FIG. 5.

The example message logger 260 of the illustrated example of FIG. 2inspects the social media messages returned by the query handler 225 formedia-exposure social media messages. For example, media of interest maybe “The Daily Show with Jon Stewart.” In such instances, amedia-exposure social media message of interest may include the text“Jon Stewart is really funny on The Daily Show right now!” and mayinclude a message timestamp indicating that the media-exposure socialmedia message was posted by the message author during broadcast of themedia of interest.

In contrast, a non-media-exposure social media message may includereference a reference to media of interest (e.g., one or more mediakeyword(s) associated with media of interest (e.g., the text “Just raninto Jon Stewart from The Daily Show at my favorite pizza parlor!”)),but does not include a characteristic indicating exposure to the mediaof interest (e.g., the message may not have been posted by the messageauthor during broadcast times associated with the television show).Moreover, additional techniques, such as natural language processing maybe used to identify whether a social media message is likely to be amedia-exposure social media message.

The example message logger 260 of FIG. 2 stores information collectedfrom media-exposure social media messages in the example media messagesdatabase 265. For example, they message logger 260 may log a mediaidentifier, a message identifier and a message author. An example datatable 500 representing example data that may be stored by the examplemessage logger 260 is shown in the illustrated example of FIG. 5.

In some examples, the message logger 260 requests impression informationrelated to identified media-exposure social media messages. In theillustrated example, the message logger 260 reads the message identifierassociated with a media-exposure social media message and prompts thequery engine 255 to generate a query (or queries) requesting impressioninformation associated with the message. In some examples, the socialmedia server 105 provides impression information associated with themessage including, for example, obfuscated usernames representing userswho were presented the media-exposure social media message. In someexamples, the message logger 260 parses the returned impressioninformation to verify that the impression information corresponds toexposure to the media of interest. The example message logger 260 ofFIG. 2 records the impression information, including the returnedusernames, in the media messages database 265.

In the illustrated example, the message logger 260 stores themedia-exposure social media message-related information in the mediamessages databases 265. For example, the message logger 260 stores amedia identifier, a message identifier, a message author identifierand/or a message viewer identifier in the media messages database 265.An example data table 500 representing example data that may be storedby the example message logger 260 is shown in the illustrated example ofFIG. 5. The example media messages database 265 may be implemented by avolatile memory (e.g., a Synchronous Dynamic Random Access Memory(SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic RandomAccess Memory (RDRAM), etc.) and/or a non-volatile memory (e.g., flashmemory). The example media messages database 265 may additionally oralternatively be implemented by one or more double data rate (DDR)memories, such as DDR, DDR2, DDR3, mobile DDR (mDDR), etc. The examplemedia messages database 265 may additionally or alternatively beimplemented by one or more mass storage devices such as hard diskdrive(s), compact disk drive(s), digital versatile disk drive(s), etc.While in the illustrated example the media messages database 265 isillustrated as a single database, the media messages database 265 may beimplemented by any number and/or type(s) of databases.

The example audience estimator 270 of the illustrated example of FIG. 2inspects the social media messages recorded in the media messagesdatabase 265 to estimate audiences for media. In the illustratedexample, the audience estimator 270 estimates an audience for a mediabased on a number of unique audience members using social media. Forexample, the audience estimator 270 of FIG. 2 may parse the social mediamessages stored in the media messages database 265 and identify themedia-exposure social media messages that reference particular media(e.g., a golf tournament) and the unique authors who postedmedia-exposure social media messages of interest. The example audienceestimator 270 of FIG. 2 stores a media identifier, the number ofmedia-exposure social media messages related to the media of interestand a number of unique authors who posted the media-exposure socialmedia messages related to the media of interest in the example audiencedatabase 275. For example, if the audience estimator 270 identifies afirst media-exposure social media message that references a golftournament and was posted by a first author, identifies a secondmedia-exposure social media message that references the golf tournamentand was posted by a second author, and identifies a third media-exposuresocial media message that references the golf tournament and was alsoposted by the first author, the example audience estimator 270 recordsin the audience database 275 that three media-exposure social mediamessages referencing a golf tournament were posted by two uniqueauthors.

Additionally or alternatively, the example audience estimator 270 ofFIG. 2 may estimate the audience for media of interest based on a numberof unique users who were presented media-exposure social media messagesof interest. For example, the audience estimator 270 may inspect themedia messages database 265 and determine that the first media-exposuresocial media message posted by the first author was presented to a firstuser, a second user and a third user, the second media-exposure socialmedia message posted by the second author was presented to the seconduser and a fourth user, and that the third media-exposure social mediamessage posted by the first author was presented to the first user, thesecond user, the third user and a fifth user. In such instances, theexample audience estimator 270 may record in the audience database 275that three media-exposure social media messages referencing a golftournament were presented to five unique users.

In some examples, the audience estimator 270 of FIG. 2 may estimate theaudience media of interest based on the number of unique authors and thenumber of unique users presented media-exposure social media messages ofinterest. For example, with reference to the above example, the audienceestimator 270 may record in the audience database 275 that threemedia-exposure social media messages referencing a golf tournament wereposted and that eight total unique audience members (e.g., three authorsplus five users who were presented the media-exposure social mediamessages of interest) were identified. Moreover, in some examples, theaudience estimator 270 may adjust the size of the audience if, forexample, an author who posted a media-exposure social media message wasalso a user who was presented a media-exposure social media message. Forexample, in the above example, if the first author who posted the firstand third media-exposure social media messages was also the fourth userwho was presented the second media-exposure social media message, thanthe total unique audience members is seven. However, any other approachto estimating audiences and/or audience sizes may additionally oralternatively be used.

In the illustrated example, the audience estimator 270 stores theaudience-related information in the audience databases 275. For example,the audience estimator 270 stores a media identifier, a number ofmedia-exposure social media messages and a total audience size relatedto media of interest (e.g., the number of unique authors and/or uniqueusers presented media-exposure social media messages of interest) in theaudience database 275. An example data table 600 representing exampledata that may be stored by the example audience estimator 270 is shownin the illustrated example of FIG. 6. The example audience databases 275may be implemented by a volatile memory (e.g., a Synchronous DynamicRandom Access Memory (SDRAM), Dynamic Random Access Memory (DRAM),RAMBUS Dynamic Random Access Memory (RDRAM), etc.) and/or a non-volatilememory (e.g., flash memory). The example audience databases 275 mayadditionally or alternatively be implemented by one or more double datarate (DDR) memories, such as DDR, DDR2, DDR3, mobile DDR (mDDR), etc.The example audience databases 275 may additionally or alternatively beimplemented by one or more mass storage devices such as hard diskdrive(s), compact disk drive(s), digital versatile disk drive(s), etc.While in the illustrated example the audience databases 275 isillustrated as a single database, the audience databases 275 may beimplemented by any number and/or type(s) of databases.

The example pairing classifier 280 of the illustrated example of FIG. 2classifies a media-pairing based on information stored in the mediamessages database 265 and the audience database 275. In the illustratedexample, the pairing classifier 280 selects first media and second mediafrom the audience database 275 and determines the number of overlappingaudience members. For example, the pairing classifier 280 may parse themedia messages database 265 for media-exposure social media messagesrelated to the first media of interest and generate a first set ofaudience members based on the unique authors of and/or the unique userswho were presented the media-exposure social media messages related tothe first media of interest. The example pairing classifier 280 may thenparse the media messages database 265 and generate a second set ofaudience members based on the unique authors of and/or the unique userswho were presented media-exposure social media messages related tosecond media of interest. The example pairing classifier 280 comparesthe audience members included in the first set and the second set toidentify overlapping users (e.g., audience members identified in thefirst set of audience members and the second set of audience members).

In the illustrated example, the pairing classifier 280 selects the firstmedia and the second media based on the different media for whichmedia-exposure social media messages were identified. In some suchexamples, the number of media-pairings may correspond to the number ofcombinations of the identified media. For example, one media-pairingwhen the number of media identified is two, three media-pairings whenthe number of media identified is three, six media-pairings when thenumber of media identified is four, etc. However, any other approach forselecting the first media and/or the second media may additionally oralternatively be used. For example, the pairing classifier 280 may notselect two episodes of the same television program as the first mediaand the second media. Excluding different airings of the same televisionshow (e.g., episodes one and two of season one of a sitcom, an episodeor media event that airs on different broadcasting networks (e.g., theState of the Union Address), a syndicated television program that airson multiple broadcasting networks, etc.) may be advantageous forconserving computing resources as the same television program is likelyto have the same audience.

Additionally or alternatively, a media-pairing may be skipped when themedia are known to be demographically dissimilar. For example, themajority of the audience of a first television program may be audiencemembers who are between the ages of 60 and 70 and the majority of theaudience of a second television program may be audience members who arebetween the ages of 13-20. In some such examples, because thedemographics for the respective media are not similar (e.g., do notoverlap), the example pairing classifier 280 may not select the firsttelevision program and the second television program for classifying.

In the illustrated example of FIG. 2, the pairing classifier 280calculates an intersection score for the media-pairing (e.g., the firstmedia and the second media) based on the number of overlapping users andtotal number of unique audience members. In the illustrated example, thepairing classifier 280 selects one of the total audience sizes for themedia of interest when calculating the intersection score. For example,the pairing classifier 280 may query and/or retrieve the total audiencesizes for the media of interest of the media-pairing (e.g., the firstmedia and the second media) from the audience database 275 and selectthe lesser of the two returned values. In the illustrated example, thepairing classifier 280 calculates the intersection score for themedia-pairing based on a ratio of the number of overlapping users andthe selected total audience size. However, any other approach tocalculate an intersection score between media-pairings may additionallyor alternatively be used. For example, the pairing classifier 280 maycalculate two intersection scores for the media-pairing (e.g., a firstintersection score corresponding to the total number of audience membersfor the first media and a second intersection score corresponding to thenumber of audience members for the second media) and select the higherof the two intersections scores to record in the media-pairings database285.

The example pairing classifier 280 records media identifiers for themedia-pairing, the number of overlapping users and the intersectionscore in the example media-pairings database 285. In some examples, ifthe number of overlapping users and/or the total number of audiencemembers for one or both media is less than a threshold (e.g., a sizethreshold), then the pairing classifier 280 discards (e.g., skips)calculating the intersection score for the media-pairing and norelationship between the first media and the second media is determined.For example, if the number of overlapping users and/or the total numberof audience members for one or both media is less than the sizethreshold, the potential gain in sales may not be cost-effective withrespect to the cost of purchasing broadcasting time for themedia-pairing and/or producing sponsored media. For example, an entity(e.g., an advertiser) may project that 10% of audience members exposedto an advertisement will buy the advertised product for $50.Furthermore, the entity may estimate the cost of producing andbroadcasting the advertisement is $100,000. In the illustrated example,the size threshold may be set to an absolute number (e.g., 20,000users), because if the number of overlapping users and/or the totalnumber of audience members is less than 20,000, the entity would notrecover the cost of producing and broadcasting the advertisement.

In the illustrated example of FIG. 2, the pairing classifier 280classifies the media-pairing by comparing the intersection scorecalculated for the media-pairing to a relationship threshold. When amedia-pairing satisfies the relationship threshold (e.g., theintersection count calculated for the first media and the second mediais greater than or equal to the relationship threshold), the pairingclassifier 280 classifies the media-pairing related media. When themedia-pairing fails to satisfy the relationship threshold (e.g., theintersection count calculated for the first media and the second mediais less than the relationship threshold), the pairing classifier 280classifies the media-pairing unrelated media. However, other approachesto differentiate between related media-pairings and unrelatedmedia-pairings may additionally or alternatively be used.

As disclosed herein, the size threshold and/or the relationshipthreshold is/are derived from an empirical study of overlapping audiencemembers using social media. For example, analysis of data sets (e.g.,test data sets, previously processed data sets, etc.) may indicate thatwhen a media-pairing has an intersection score that is less than 70%,the effect the media included in the media-pairing has on each other maynot be statistically significant. In some such examples, therelationship threshold may be set to a default value (e.g., 70%).However, the size threshold and/or the relationship threshold may bederived in any other fashion. For example, the relationship thresholdmay be determined by preferences specified by the AME 115 and/or anentity requesting a report. In some examples, the relationship thresholdmay adjust based on characteristics of the first media and/or the secondmedia. For example, if the first media and/or the second media includesan appearance by a positive influence guest (e.g., a guest such as acelebrity, a politician, an athlete, etc. whose appearance in mediacauses an increase in viewership of the media), the pairing classifier280 may increase the relationship threshold (e.g., +5%). If the firstmedia and/or the second media includes a negative influence guest (e.g.,a guest whose appearance in media causes a decrease in viewership of themedia), the pairing classifier 280 may decrease the relationshipthreshold (e.g., −5%). In some examples, a planned cross-over betweenthe first media and the second media may cause the pairing classifier toincrease the relationship threshold (e.g., +15%). In some examples, ifthe first media and/or the second media is a one-time event (e.g., aleague championship, an awards ceremony, etc.), the pairing classifier280 may decrease the relationship threshold (e.g., −30%). However, anyother qualifications for adjusting the size threshold and/or therelationship threshold may additionally or alternatively be used. Forexample, the relationship threshold may adjust based on the cost ofairing sponsored media during one or both media in the media-pairing.For example, the potential gains in sales due to broadcasting during amedia event (e.g., a one-time event such as a league championship, anawards ceremony, etc.) may not be cost-effective with respect to thecost of purchasing broadcast time during the television program.

The example pairing classifier 280 of the illustrated example stores themedia-pairing classifications in the media-pairings database 285. Theexample pairing classifier 280 classifies and/or re-classifies themedia-pairings at periodic intervals (e.g., every 24 hours, everyMonday, etc.). However, any other time period may additionally oralternatively be used for classifying and/or re-classifying themedia-pairings. For example, the pairing classifier 280 may classifyand/or re-classify the media-pairings at aperiodic intervals (e.g., whenrequested, each time the media-pairings database 285 is updated (e.g., anew media profile is added to the media profile database 250, a newentry is added to the media messages database 265 by the message logger260, a new entry is added to the audience database 275 by the audienceestimator 270), etc.) and/or as a one-time event.

An example data table 700 representing example data that may be storedin the example media-pairings database 285 is shown in the illustratedexample of FIG. 7. The example media-pairings database 285 may beimplemented by a volatile memory (e.g., a Synchronous Dynamic RandomAccess Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUSDynamic Random Access Memory (RDRAM), etc.) and/or a non-volatile memory(e.g., flash memory). The example media-pairings database 285 mayadditionally or alternatively be implemented by one or more double datarate (DDR) memories, such as DDR, DDR2, DDR3, mobile DDR (mDDR), etc.The example media-pairings database 285 may additionally oralternatively be implemented by one or more mass storage devices such ashard disk drive(s), compact disk drive(s), digital versatile diskdrive(s), etc. While in the illustrated example the media-pairingsdatabase 285 is illustrated as a single database, the media-pairingsdatabase 285 may be implemented by any number and/or type(s) ofdatabases.

The example reporter 290 of the illustrated example of FIG. 2 generatesone or more reports classifying media-pairings as related media orunrelated media. In the illustrated example of FIG. 2, the examplereporter 290 identifies one or more media-pairing(s) of interest andprovides the media identifiers to the pairing classifier 280 based on,for example, specifications provided by an entity requesting the report(e.g., a sponsored media proprietor). In some examples, the reports arepresented to the proprietors of media included in the media-pairings.

In some examples, the reporter 290 may provide a variety of use casesbased on the related media-pairings. As an illustrative example,consider an example in which a relatively large percentage (e.g., 71%)of audience members of a broadcast of a golf tournament are alsoaudience members of a daytime family court show. The example reporter290 may identify the related media-pairing (e.g., the golf tournamentbroadcast and daytime family court show) and provide recommendations toa proprietor of golf equipment.

In some examples, the reporter 290 may recommend cross-promotionopportunities available to the golf equipment proprietors. In someexamples, the cross-promotion opportunities may include first and secondmedia that are known (e.g., the daytime family court show and anotherdaytime program that is broadcast by the same network that broadcaststhe daytime family court show). Additionally or alternatively, thecross-promotion opportunities may include first and second media thatare not known (e.g., the golf tournament is broadcast by a first networkand the daytime family court show is broadcast by a second network). Forexample, it may be advantageous for the golf equipment proprietors tobroadcast sponsored media for their golf equipment during futurebroadcasts of golf tournaments and during broadcasts of the daytimefamily court show. In some such examples, the proprietors of the golfequipment may increase exposure to their golf equipment.

In another illustrative example, the reporter 290 may recommendbroadcasting their golf equipment sponsored media during broadcasts ofthe daytime family court show instead of during future broadcasts ofgolf tournaments. In some such examples, because of the relatively largeoverlap of audience members of related media, the golf equipmentproprietors may be able to reach a similar audience for less money.

In another illustrative example, the reporter 290 may recommendbroadcasting their golf equipment sponsored media during broadcasts ofmedia based on one or more intermediary media. For example, in the aboveexample, a first media-pairing includes the golf tournament broadcastand the daytime family court show. In some examples, the reporter 290may identify a second media-pairing including the daytime family courtshow. For example, the reporter 290 may determine that a relativelylarge percentage (e.g., 75%) of audience members of the daytime familycourt show are also audience members of an animal adventures serial. Insuch instances, although the number of audience members who watch thegolf tournament broadcast and the animal adventures serial may not belarge enough to indicate a relationship between the media-pairing (e.g.,the number of overlapping audience members does not satisfy arelationship threshold), the reporter 290 may recommend broadcastingduring the animal adventures serial based on the second media-pairingrelationship.

FIG. 3 is an example data table 300 that may be stored by the examplesocial media server 105 of FIGS. 1 and/or 2 to store social mediamessages. The example data table 300 of the illustrated example of FIG.3 includes a message identifier column 310, a message posting timestampcolumn 320, a message column 330 and a message author column 340. Theexample message identifier column 310 indicates an identifier of amessage posted using the social media service provided by the socialmedia server 105. In the illustrated example, the message identifier isa unique serial identifier. However, any other approach uniquelyidentifying a message may additionally or alternatively be used.

The example message posting timestamp column 320 indicates a date and/ortime at which the message identified by the message identifier column310 was posted on the social media service. The example message column330 indicates the message that was posted to the social media service.The example message author column 340 identifies a username of the userthat posted the social media message to the social media serviceprovided by the social media server 105. The example message authorcolumn 340 is used by the social media server 105 when providing socialmedia messages to users.

The example data table 300 of the illustrated example of FIG. 3 includesthree example rows 360, 370, 380 corresponding to three example socialmedia messages. The example first row 360 indicates that a messagehaving an identifier of “0001” and a text of “Going to see his when itcomes out! So funny on #TalkShow” was posted by “@User-123” at 10:12 PMon Aug. 6, 2015. The example second row 370 indicates that a messagehaving an identifier of “0002” and a text of “That's why she is the best#golfer in the world! #Championships” was posted by “@Golf-Fan” at 3:23PM on Aug. 11, 2015. The example third row 380 indicates that a messagehaving an identifier of “0003” and a text of “Why can't they all getalong? #FamilyCourt” was posted by “@User-ABC” at 11:11 AM on Aug. 15,2015. While three example messages are represented in the example datatable 300 of FIG. 3, more or fewer social media messages may berepresented in the example data table 300 corresponding to the manymessages posted to the social media service provided by the social mediaserver 105.

FIG. 4 represents an example data table 400 that may be stored by theAME 115 of FIGS. 1 and/or 2 representing media presentation time rangesand media keywords associated with different media. The example datatable 400 of the illustrated example of FIG. 4 is stored in the examplemedia profile database 250. The example data table 400 of theillustrated example of FIG. 4 includes a media identifier column 410, amedia time range column 420 and a media keyword(s) column 430. The mediaidentifier column 410 identifies media (e.g., a television show, amovie, etc.). In the illustrated example, television shows aired via abroadcast television system are identified. However, any other type ofmedia may additionally or alternatively be used. The example media timerange column 420 includes media presentation time ranges that may beused by the example central facility 110 to determine whethermedia-exposure social media messages should be requested in associatedwith a media presentation. In some examples, the media time ranges arerepeated time ranges in that the media is expected to air periodically(e.g., once a week, once a month, every day, on weekdays, etc.).However, any other approach to identifying when media is expected toair, periodic and/or aperiodic, may additionally or alternatively beused.

The example media keywords of the example media keyword(s) column 430are used when querying the social media server 105 to retrieve socialmedia messages that are media-exposure social media messages that areassociated with particular media (e.g., the media identified by themedia identifier column 410). In the illustrated example, the mediakeywords are represented using a text string, with various keywordsand/or phrases separated by semicolons. However, any other approach tostoring the media keywords may additionally or alternatively be used.

The example data table 400 of the illustrated example of FIG. 4 includesthree example rows 450, 460, 470. The example first row 450 identifiesthat the Aug. 6, 2015, airing of “Talk Show” has a time range between9:30 PM and 10:30 PM (CT) on weekdays and is identifiable using themedia keywords “Talk Show; Comedian; Funny.” The example second row 460identifies that the Aug. 11, 2015, airing of “Golf Tournament” has atime range between 4:00 PM and 7:00 PM (CT) on Aug. 11, 2015, and isidentifiable using the media keywords “Golf; Championships; Golfer.” Theexample third row 470 identifies that the Aug. 15, 2015, airing of“Family Court” has a time range between 11:00 AM and 12:00 PM (CT) onweekdays, and is identifiable using the media keywords “Family; Daytime;Court.”

FIG. 5 is an example data table 500 representing example social mediamessages and impression information associated with the social mediamessages that may be supplied to the message logger 260 of the examplecentral facility 110 of FIGS. 1 and/or 2 by the social media server 105of FIGS. 1 and/or 2. The example data table 500 of the illustratedexample of FIG. 5 includes a media identifier column 510, a messageidentifier column 520, a message author column 530 and a message viewercolumn 540.

The example media identifier column 510 identifies media (e.g., atelevision show, a movie, etc.). In the illustrated example, televisionshows aired via a broadcast television system are identified. However,any other type of media may additionally or alternatively be used. Theexample message identifier column 520 indicates an identifier of amessage posted using the social media service provided by the socialmedia server 105. The message identifier is useful to the examplecentral facility 110 to map impression information to a previouslyprocessed social media message. Reusing previously identified socialmedia messages reduces the amount of processing requirements of thecentral facility 110. The example message author column 530 identifies ausername of the user that posted the social media message to the socialmedia service provided by the social media server 105. The examplemessage viewer column 540 identifies a username of a user that waspresented the social media message.

The example data table 500 includes three example rows corresponding tosocial media message information and/or impression information forsocial media messages returned by the social media server 105. Anexample first row 560 indicates that the social media messagecorresponding message identifier “0002” references the “08/11/2015 GolfTournament” and was posted by “@Golf-Fan.” The example first row 560does not identify a user who was presented the social media message. Anexample second row 570 indicates that the social media messagecorresponding to the message identifier “0002” references the“08/11/2015 Golf Tournament” and was presented to “@Sports_Star.” Anexample third row 580 indicates that the social media messagecorresponding to the message identifier “0002” references the“08/11/2015 Golf Tournament” and was presented to “@User-ABC.”

FIG. 6 represents an example media audience data table 600 that may bestored by the example AME 115 of FIG. 1 and/or representing exampleestimates of audiences for media. The example data table 600 of theillustrated example of FIG. 6 is stored in the example audience database275. The example data table 600 of the illustrated example of FIG. 6includes an example media identifier column 610, an example number ofmessages identifier column 620 and an example number of unique audiencemembers identifier column 630. The example media identifier column 610identifies media (e.g., a television show, a movie, etc.). In theillustrated example, television shows aired via a broadcast televisionsystem are identified. However, any other type of media may additionallyor alternatively be used.

The example number of messages identifier column 620 identifies thenumber of media-exposure social media messages were identified for themedia identified in the media identifier column 610. The example numberof unique audience members identifier column 630 identifies an estimatedaudience size for the media identified in the media identifier column610. In the illustrated example, the number of unique message authorsreferencing the media identified in the media identifier column 610 insocial media messages is identified. However, any other type ofestimation technique may additionally or alternatively be used. Forexample, the number of unique audience members may represent the numberof unique message authors and/or the number of unique users who werepresented media-exposure social media messages of interest.

In the illustrated example of FIG. 6, the example data table 600includes three example rows 650, 660, 670. The first example row 650indicates that a total number of 13,597 social media messagesreferencing the Aug. 7, 2015, broadcast of “Talk Show” were posted usingthe social media service provided by the social media server 105 by atotal number of 9,541 different users. The second example row 660indicates that a total number of 105,997 social media messagesreferencing the Aug. 11, 2015, broadcast of “Golf Tournament” wereposted using the social media service provided by the social mediaserver 105 by a total number of 68,753 different users. The thirdexample row 670 indicates that a total number of 590 social mediamessages referencing the Aug. 15, 2015, broadcast of “Family Court” wereposted using the social media service provided by the social mediaserver 105 by a total number of 570 different users.

FIG. 7 represents an example data table 700 that may be stored by theexample AME 115 of FIGS. 1 and/or 2 representing identifiedmedia-pairings and whether the respective media are related. The exampledata table 700 of the illustrated example of FIG. 7 is stored in theexample media-pairings database 285. The example data table 700 of theillustrated example of FIG. 7 includes an example first media identifiercolumn 705, an example second media identifier column 710, an examplenumber of overlapping audience members identifier column 715, an examplesmallest number of unique audience members identifier column 720, anexample pairing score identifier column 725, an example thresholdapplied identifier column 730 and an example media related identifiercolumn 735.

The example first media identifier column 705 and the example secondmedia identifier column 710 identify the media of interest,respectively, included in the media-pairing. The example number ofoverlapping audience members identifier column 715 represents the numberof users identified as audience members for both media identified in themedia identifier columns 705, 710. In the illustrated example, thenumber of overlapping users corresponds to the number of users whoposted one or more media-exposure social media message(s) referencingthe first media identified in the first media identifier column 705 andwho also posted one or more media-exposure social media message(s)referencing the second media identified in the second media identifiercolumn 710. For example, the pairing classifier 280 may compare theusernames included in a first set of usernames (e.g., audience members)associated with the first media and the usernames included in a secondset of usernames associated with the second media and count the numberof overlapping usernames. However, any other approach for determining anumber of overlapping audience members may additionally or alternativelybe used.

The example smallest number of unique audience members identifier column720 represents the smaller number of total audience members for themedia identified in the media identifier columns 705, 710. In theillustrated example, the smallest number of unique audience members isdetermined based on the number of unique audience members listed in theexample data table 600 of FIG. 6 for the respective media.

The example pairing score identifier column 725 identifies theintersection in audience members for the media identified in the mediaidentifiers columns 705, 710. In the illustrated example, the pairingscore represents the number of overlapping audience members identifiedin the example number of overlapping audience members identifier column715 relative to the number of unique audience members identified in theexample smallest number of unique audience members identifier column720. However, any other approach for calculating a pairing score mayadditionally or alternatively be used.

The example threshold applied identifier column 730 identifies therelationship threshold value used in determining whether the mediaidentified in the media identifier columns 705, 710 are related. Theexample media related identifier column 725 identifies the relationshipstatus for the media identified in the media identifier columns 705, 710based on the pairing score identified in the pairing score identifiercolumn 725 and the threshold applied identifier column 730.

In the illustrated example of FIG. 7, the example classifiedmedia-pairings data table 700 includes three example rows 750, 760, 770.The first example row 750 indicates a media-pairing including the Aug.7, 2015, broadcast of “Talk Show” and the Aug. 11, 2015, broadcast of“Golf Tournament.” The example first row 750 indicates that “7%” of theaudience of the “Talk Show” broadcast (“9,541” users) were also audiencemembers of the “Golf Tournament,” which was broadcast four days afterthe “Talk Show.” In the illustrated example, because the pairing score(“7%”) fails to satisfy the relationship threshold (“70%”), the mediaindicated in the first example row 750 are not classified as relatedmedia.

The second example row 760 indicates a media-pairing including the Aug.7, 2015, broadcast of “Talk Show” and the Aug. 15, 2015, broadcast of“Family Court.” The example second row 760 indicates that “27%” of theaudience of the “Family Court” broadcast (“570” users) were alsoaudience members of the “Talk Show,” which was broadcast eight daysbefore the “Family Court.” In the illustrated example, because thepairing score (“27%”) fails to satisfy the relationship threshold(“80%”), the media indicated in the second example row 760 are notclassified as related media.

The third example row 770 indicates a media-pairing including the Aug.11, 2015, broadcast of “Golf Tournament” and the Aug. 15, 2015,broadcast of “Family Court.” The example third row 770 indicates that“71%” of the audience of the “Family Court” broadcast (“570” users) werealso audience members of the “Golf Tournament,” which was broadcast fourdays before the “Family Court.” In the illustrated example, because thepairing score (“71%”) satisfies the relationship threshold (“70%”), themedia indicated in the third example row 770 are classified as relatedmedia.

While an example manner of implementing the social media server 105 FIG.1 is illustrated in FIG. 2, and an example manner of implementing thecentral facility 110 of FIG. 1 is illustrated in FIG. 2, one or more ofthe elements, processes and/or devices illustrated in FIG. 2 may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the example social message interface 205, theexample social message database 210, the example social message exposuredatabase 215, the example messages identifier 220, the example queryhandler 225 and/or, more generally, the example social media server 105of FIG. 1, and/or the example media profile database 250, the examplequery engine 255, the example message logger 260, the example mediamessages database 265, the example audience estimator 270, the exampleaudience database 275, the example pairing classifier 280, the examplemedia-pairings database 285, the example reporter 290 and/or, moregenerally, the example central facility 110 of FIG. 1 may be implementedby hardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the example socialmessage interface 205, the example social message database 210, theexample social message exposure database 215, the example messagesidentifier 220, the example query handler 225 and/or, more generally,the example social media server 105 of FIG. 1, and/or the example mediaprofile database 250, the example query engine 255, the example messagelogger 260, the example media messages database 265, the exampleaudience estimator 270, the example audience database 275, the examplepairing classifier 280, the example media-pairings database 285, theexample reporter 290 and/or, more generally, the example centralfacility 110 of FIG. 1 could be implemented by one or more analog ordigital circuit(s), logic circuits, programmable processor(s),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)).When reading any of the apparatus or system claims of this patent tocover a purely software and/or firmware implementation, at least one ofthe example social message interface 205, the example social messagedatabase 210, the example social message exposure database 215, theexample messages identifier 220, the example query handler 225 and/or,more generally, the example social media server 105 of FIG. 1, and/orthe example media profile database 250, the example query engine 255,the example message logger 260, the example media messages database 265,the example audience estimator 270, the example audience database 275,the example pairing classifier 280, the example media-pairings database285, the example reporter 290 and/or, more generally, the examplecentral facility 110 of FIG. 1 is/are hereby expressly defined toinclude a tangible computer readable storage device or storage disk suchas a memory, a digital versatile disk (DVD), a compact disk (CD), aBlu-ray disk, etc. storing the software and/or firmware. Further still,the example social media server 105 of FIG. 1 and/or the example centralfacility 110 of FIG. 1 may include one or more elements, processesand/or devices in addition to, or instead of, those illustrated in FIG.2, and/or may include more than one of any or all of the illustratedelements, processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the example social media server 105 of FIGS. 1 and/or 2is/are shown in FIGS. 8 and/or 9. In these examples, the machinereadable instructions comprise a program(s) for execution by a processorsuch as the processor 1412 shown in the example processor platform 1400discussed below in connection with FIG. 14. The program(s) may beembodied in software stored on a tangible computer readable storagemedium such as a CD-ROM, a floppy disk, a hard drive, a digitalversatile disk (DVD), a Blu-ray disk, or a memory associated with theprocessor 1412, but the entire program(s) and/or parts thereof couldalternatively be executed by a device other than the processor 1412and/or embodied in firmware or dedicated hardware. Further, although theexample program(s) is/are described with reference to the flowchartsillustrated in FIGS. 10-12 and/or 13, many other methods of implementingthe example central facility 110 may alternatively be used. For example,the order of execution of the blocks may be changed, and/or some of theblocks described may be changed, eliminated, or combined.

FIG. 8 is a flowchart representative of example machine-readableinstructions 800 that may be executed by the example social media server105 of FIGS. 1 and/or 2 to reply to a request for social media messagesof interest. The example instructions 800 of the illustrated example ofFIG. 8 begin at block 802 when the example query handler 225 of theexample social media server 105 of FIG. 2 receives a request for socialmedia message information from the query engine 255 of the examplecentral facility 110 of FIG. 2. In the illustrated example, the examplequery includes a request that all social media messages, where thesocial media message includes one or more media keyword(s) and wasposted by a user between a first time and a second time, be presented tothe query engine 255. However, any other approach to querying the socialmedia server 105 may additionally or alternatively be used.

In the illustrated example, the query is received via the network 130.However, the query may be received in any other fashion. Moreover, theexample query of the illustrated example is received as the results arebeing requested. That is, the example query engine 255 expects animmediate response to the query. However, the query may be receivedahead of time, and the example query handler 225 may await anappropriate time to respond to the query.

At block 804, the example messages identifier 220 of FIG. 2 identifies amessage of interest. In the illustrated example, the example messagesidentifier 220 inspects the example social message database 210 todetermine whether the message is responsive to the query. For example,the messages identifier 220 identifies whether a social media messageincludes at least one media keyword. In the illustrated example, themedia keywords are text strings that are associated with particularmedia of interest. An example data table 400 including example mediaidentifiers and their associated media keywords is shown in theillustrated example of FIG. 4. In the illustrated example, the messagesidentifier 220 uses regular expressions to determine whether the socialmedia message includes the at least one media keyword. However, anyother approach to identifying whether keywords are present in a socialmedia message may additionally or alternatively be used.

At block 806, the example messages identifier 220 determines whether theidentified message was posted between a first time and a second time. Inthe illustrated example, the first time and the second time represent amedia presentation time range during which corresponding media isbroadcast. An example data table 400 including example media identifiersand their associated media presentation time ranges is shown in theillustrated example of FIG. 4. In the illustrated example, the messagesidentifier 220 inspects the example social message database 210 todetermine whether the message is responsive to the query. For example,the messages identifier 220 identifies whether the message was postedduring the media presentation time range.

If, at block 806, the messages identifier 220 determines that theidentified message was not presented between the first time and thesecond time, then, at block 810, the messages identifier 220 determineswhether additional messages having at least one media keyword exists.If, at block 810, such messages exist, then, at block 804, the messagesidentifier 220 identifies those messages and, at block 806, the messagesidentifier 220 determines whether those messages were posted between afirst time and a second time.

If, at block 806, the identified social media message was posted betweenthe first time and the second time, then, at block 808, the examplequery handler 225 provides the social media message to the query engine255. After the example query handler 225 provides the social mediamessage at block 808 or if the messages identifier 220 determines thatthe identified social media message was not posted between the firsttime and the second time at block 806, then, at block 810, the examplemessages identifier 220 determines whether additional messages having atleast one media keyword exist. If, at block 810, the example messagesidentifier 220 determines additional social media messages having atleast one media keyword do not exist, control returns to block 802 andthe example query handler 225 awaits further requests for social mediamessages.

While in the illustrated example, the example instructions 800 of FIG. 8represent a single iteration of providing requested social mediamessages of interest, in practice, the example instructions 800 of theillustrated example of FIG. 8 may be executed in parallel (e.g., inseparate threads) to allow the social media server 105 to handlemultiple requests for social media messages of interests at a time.

FIG. 9 is a flowchart representative of example machine-readableinstructions 900 that may be executed by the example social media server105 of FIGS. 1 and/or 2 to reply to a request for impression informationrelated to social media messages of interest. For example, social mediaserver 105 may receive a request to provide usernames of users who werepresented media-exposure social media messages of interest duringbroadcast of the corresponding media. The example instructions 900 ofthe illustrated example of FIG. 9 begin at block 902 when the examplequery handler 225 of the example social media server 105 of FIG. 2receives a request for impression information related to social mediamessages of interest from the query engine 255 of the example centralfacility 110 of FIG. 2. In the illustrated example, the example queryincludes a message identifier used to identify a social media message ofinterest. However, any other approach to querying the social mediaserver 105 may additionally or alternatively be used.

In the illustrated example, the query is received via the network 130.However, the query may be received in any other fashion. Moreover, theexample query of the illustrated example is received as the results arebeing requested. That is, the example query engine 255 expects animmediate response to the query. However, the query may be receivedahead of time, and the example query handler 225 may await anappropriate time to respond to the query.

At block 904, the example messages identifier 220 of FIG. 2 identifies amessage of interest. In the illustrated example, the example messagesidentifier 220 inspects the example social message exposure database 215to determine whether the message is responsive to the query. Forexample, the messages identifier 220 identifies whether the social mediamessage has at least one impression between a first time and a secondtime. In the illustrated example, the first time and the second timerepresent a media presentation time range during which correspondingmedia is broadcast. An example data table 400 including example mediaidentifiers and their associated media presentation time ranges is shownin the illustrated example of FIG. 4.

If, at block 906, the example messages identifier 220 determined thatthe social media message of interest did not have an impression duringthe media presentation time range of the media of interest, then, atblock 912, the messages identifier 220 determines whether additionalsocial media messages of interest exists. If, at block 912, suchmessages exist, then, at block 904, the messages identifier 220identifies those messages and, at block 906, the messages identifier 220determines whether those messages of interest have an impression betweena first time and a second time.

If, at block 906, the identified social media message had an impressionbetween the first time and the second time, then, at block 908, theexample query handler 225 provides the message information to the queryengine 255. For example, the query handler 225 may return the usernameof the user who was presented the social media message of interest, atimestamp indicating when the user was presented the social mediamessage of interest, demographic information (e.g., age, gender,occupation, etc.) associated with the user. After the example queryhandler 225 provides the social media message at block 908, then, atblock 910, the example messages identifier 220 determines whether thereis another impression associated with the social media message ofinterest to process. If, at block 910, the messages identifier 220determined that there is another impression to process, then controlreturns to block 906 to determine whether the impression was between thefirst time and the second time.

If, at block 906, the messages identifier 220 determined that theimpression was not between the first time and the second time, or, if,at block 910, the messages identifier 220 determined that the socialmedia message of interest did not have another impression to process,then, control proceeds to block 912 to determine whether there isanother social media message of interest to process. If, at block 912,the example messages identifier 220 determined additional social mediamessages of interest do not exist, control returns to block 902 and theexample query handler 225 awaits further requests for impressioninformation related to social media messages of interest.

While in the illustrated example, the example instructions 900 of FIG. 9represent a single iteration of providing requested social mediamessages of interest, in practice, the example instructions 900 of theillustrated example of FIG. 9 may be executed in parallel (e.g., inseparate threads) to allow the social media server 105 to handlemultiple requests for social media messages of interests at a time.

Flowcharts representative of example machine readable instructions forimplementing the example central facility 110 of FIGS. 1 and/or 2 is/areshown in FIGS. 10-12 and/or 13. In these examples, the machine readableinstructions comprise a program(s) for execution by a processor such asthe processor 1512 shown in the example processor platform 1500discussed below in connection with FIG. 15. The program(s) may beembodied in software stored on a tangible computer readable storagemedium such as a CD-ROM, a floppy disk, a hard drive, a digitalversatile disk (DVD), a Blu-ray disk, or a memory associated with theprocessor 1512, but the entire program(s) and/or parts thereof couldalternatively be executed by a device other than the processor 1512and/or embodied in firmware or dedicated hardware. Further, although theexample program(s) is/are described with reference to the flowchartsillustrated in FIGS. 10-12 and/or 13, many other methods of implementingthe example central facility 110 may alternatively be used. For example,the order of execution of the blocks may be changed, and/or some of theblocks described may be changed, eliminated, or combined.

As mentioned above, the example processes of FIGS. 8 and/or 9 and/or theFIGS. 10-12 and/or 13 may be implemented using coded instructions (e.g.,computer and/or machine readable instructions) stored on a tangiblecomputer readable storage medium such as a hard disk drive, a flashmemory, a read-only memory (ROM), a compact disk (CD), a digitalversatile disk (DVD), a cache, a random-access memory (RAM) and/or anyother storage device or storage disk in which information is stored forany duration (e.g., for extended time periods, permanently, for briefinstances, for temporarily buffering, and/or for caching of theinformation). As used herein, the term tangible computer readablestorage medium is expressly defined to include any type of computerreadable storage device and/or storage disk and to exclude propagatingsignals and to exclude transmission media. As used herein, “tangiblecomputer readable storage medium” and “tangible machine readable storagemedium” are used interchangeably. Additionally or alternatively, theexample processes of FIGS. 8 and/or 9 and/or the FIGS. 10-12 and/or 13may be implemented using coded instructions (e.g., computer and/ormachine readable instructions) stored on a non-transitory computerand/or machine readable medium such as a hard disk drive, a flashmemory, a read-only memory, a compact disk, a digital versatile disk, acache, a random-access memory and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm non-transitory computer readable medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, when the phrase “at least” is used as the transition termin a preamble of a claim, it is open-ended in the same manner as theterm “comprising” is open ended. Comprising and all other variants of“comprise” are expressly defined to be open-ended terms. Including andall other variants of “include” are also defined to be open-ended terms.In contrast, the term consisting and/or other forms of consist aredefined to be close-ended terms.

FIG. 10 is a flowchart representative of example machine-readableinstructions 1000 that may be executed by the example central facility110 of FIGS. 1 and/or 2 to identify co-relationships between media usingsocial media. The example process 1000 of the illustrated example ofFIG. 10 begins at block 1002 when the example central facility 110identifies media in a social media message. For example, the messagelogger 260 (FIG. 2) may parse a social media message for reference tomedia based on a media keyword. In some examples, the central facilityrequests from the social media server 105 social media messages relatedto media of interest. For example, the query engine 255 (FIG. 2) mayconsult the example data table 400 of the illustrated example of FIG. 4to identify media keywords that are used to associate social mediamessages with particular media. Example responses to the request may beformatted in a manner described in connection with the example datatable 500 of FIG. 5. An example approach to identifying social mediamessages of interest is described below in connection with FIG. 11.

At block 1004, the example central facility 110 estimates an audienceassociated with the identified media. For example, the audienceestimator 270 (FIG. 2) may analyze additional social media messagesassociated with the media and identify unique audience members of themedia. In the illustrated example, the audience estimator 270 identifiesunique audience members based on identified authors of media-exposuresocial media messages of interest. Additionally or alternatively, theaudience estimator 270 may identify unique audience members based onidentified users of social media who were presented the media-exposuresocial media messages of interest. Example estimates for audiences ofmedia may be stored in a manner described in connection with the exampledata table 6 of FIG. 6. An example approach to estimating mediaaudiences is described below in connection with FIG. 12.

At block 1006, the example central facility 110 identifies amedia-pairing for processing. For example, the pairing classifier 280(FIG. 2) may parse the media audience estimates and select two media forprocessing. In some examples, the pairing classifier 280 may excludemedia from media-pairings when the media fails to satisfy a sizethreshold. For example, when the size of the estimated audience for aparticular media is less than a size threshold (e.g., 200 audiencemembers), the pairing classifier 280 may not include the media in amedia-pairing for classification. In some examples, the pairingclassifier 280 may identify a media-pairing by identifying media thatsatisfy constraints for a media campaign.

At block 1008, the example central facility 110 classifies themedia-pairing based on occurrences of audience members in the audiencesof the respective media. For example, the pairing classifier 280 maycalculate a pairing score based on identified audience members includedin a listing of audience members associated with first media of themedia-pairing and a listing of audience members associated with secondmedia of the media-pairing. In the illustrated example, the pairingclassifier 280 classifies the first media and the second media asrelated or unrelated based on a comparison of the pairing score to arelationship threshold. An example approach to classifying theidentified media-pairing is described below in connection with FIG. 13.

At block 1010, the example central facility 110 generates a report. Forexample, the reporter 290 (FIG. 2) may generate a report identifyingmedia-pairings in which the media were classified as related media orclassified as unrelated media. In some examples, the reporter 290 mayselect the media-pairings to include in the report based on the entityrequesting the report. For example, the reporter 290 may identifymedia-pairings including related media that are beneficial to aproprietor of golf equipment. In some examples, the reporter 290 mayinclude a variety of uses bases based on the related media-pairings.

At block 1012, the example central facility 110 determines whether tocontinue identifying co-relationships between media using social media.If, at block 1012, the central facility 110 determined to continueidentifying co-relationships between media using social media, thencontrol returns to block 1002 to identify media in a social mediamessage(s). If, at block 1010, the central facility 110 determined notto continue identifying co-relationships between media using socialmedia, the example process 1000 of FIG. 10 ends.

FIG. 11 is a flowchart representative of example machine-readableinstructions 1100 that may be executed by the example central facility110 of FIGS. 1 and/or 2 to record message information related tomedia-exposure social media messages of interest. The example process1100 of the illustrated example of FIG. 11 begins at block 1102 when theexample message logger 260 of the central facility 110 identifies asocial media message to be processed. For example, the message logger260 may receive the social media message from the example query handler225 of the example social media server 105 of FIG. 2. At block 1104, theexample message logger 260 processes the social media message for media.In the illustrated example, the message logger 260 utilizes naturallanguage processing techniques to determine whether the text of themessage indicates exposure to the corresponding media.

At block 1106, the message logger 260 determines whether the socialmedia message is a media-exposure social media message. If, at block1106, the message logger 260 did not identify exposure to media ofinterest in the social media message, then the social media message isnot a media-exposure social media message and control proceeds to block1116 to determine whether another social media message to processexists.

If, at block 1106, the example message logger 260 determined that thesocial media message is a media-exposure social media message, then, atblock 1108, the example message logger 260 logs the identified media inthe example media messages database 265 of the central facility 110 ofFIG. 2, along with a message identifier for the social media message andthe posting user (e.g., the message author).

At block 1110, the example message logger 260 retrieves impressioninformation associated with the social media message. For example, themessage logger 260 may request the social media server 105 provide allimpression information associated with users presented the social mediamessage. At block 1112, the example message logger 260 determineswhether the impression information indicates exposure to media ofinterest. For example, the message logger 260 may compare a timestamp ofthe impression with a media time range associated with the media ofinterest. If, at block 1112, the message logger 260 determined that theimpression information indicates exposure to media of interest, then, atblock 1114, the message logger 260 records the impression information.For example, the message logger 260 may log the username associated withthe impression.

If, at block 1112, the message logger 260 determined that the impressioninformation did not indicate exposure to media of interest, of, afterthe message logger 260 records the impression information at block 1114,then, at block 1116, the message logger 260 determines whether there isanother impression to process. If, at block 1116, the message logger 260determined that there is additional impression information to process,then control returns to block 1112 to determine whether the impressioninformation indicates exposure to media of interest.

If, at block 1116, the message logger 260 determined that there is notadditional impression information to process, of, if at block 1106, themessage logger 260 determined that the social media message did notreference exposure to media, then, at block 1118, the message logger 260determines whether another social media message to process exists. If,at block 1118, the example message logger 260 determined another socialmedia message to process exists, then control returns to block 1102 andthe message logger 260 awaits further social media messages to process.Otherwise, the example process 1100 of FIG. 11 ends.

FIG. 12 is a flowchart representative of example machine-readableinstructions 1200 that may be executed by the example central facility110 of FIGS. 1 and/or 2 to estimate audiences for media of interest. Theexample process 1200 of the illustrated example of FIG. 12 begins atblock 1202 when the example audience estimator 270 of the centralfacility 110 identifies media to be processed. For example, the audienceestimator 270 may parse the media messages database 265 of the centralfacility 110 for media to process. At block 1204, the example audienceestimator 270 identifies authors associated with social media messagesof interest. For example, the audience estimator 270 may parse the mediamessages database 265 to identify media-exposure social media messagesthat reference the identified media. At block 1206, the example audienceestimator 270 identifies users who were presented with the social mediamessages of interest. For example, the audience estimator 270 may parsethe media messages database 265 to identify impression informationassociated with the media-exposure social media messages that referencethe identified media.

At block 1208, the example audience estimator 270 estimates an audiencefor the identified media. For example, the audience estimator 270 mayremove duplicate usernames from the identified audience members. Atblock 1210, the example audience estimator 270 records the estimatedaudience for the media of interest. For example, the audience estimator270 may log the media identifier, the number of messages referencing theidentified media and the number of unique audience members in theexample audience database 275 of the central facility 110.

At block 1212, the example audience estimator 270 determines whetherother media to process exists. If, at block 1212, the example audienceestimator 270 determined other media to process exists, then controlreturns to block 1202 and the audience estimator 270 awaits furthermedia to process. Otherwise, the example process 1200 of FIG. 12 ends.

FIG. 13 is a flowchart representative of example machine-readableinstructions 1300 that may be executed by the example central facility110 of FIGS. 1 and/or 2 to classify identified media-pairings. Theexample process 1300 of the illustrated example of FIG. 13 begins atblock 1302 when the example pairing classifier 280 of the examplecentral facility 110 of FIG. 2 identifies a media-pairing to process.For example, the pairing classifier 280 may parse the audience database275 of the central facility 110 and select first and second media toprocess. In some examples, the pairing classifier 280 may perform a sizecheck to confirm that the number of audience members for the first mediaand the second media satisfy a size threshold (e.g., is a minimum size).

At block 1304, the example pairing classifier 280 compares the usernamesof the audience members included in the audience of the first media andthe second media. At block 1306, the example pairing classifier 280identifies overlapping audience members based on the comparison. Forexample, the pairing classifier 280 may identify the usernames includedin the audience of the first media and the audience of the second media.

At block 1308, the example pairing classifier 280 calculates a pairingscore for the media-pairing. In the illustrated example, the pairingclassifier 280 calculates the pairing score based on the number ofoverlapping audience members and the number of social media messagesidentified for the respective media. In the illustrated example, theexample pairing classifier 280 selects the lesser of the two socialmedia message counts in calculating the pairing score for themedia-pairing.

At block 1310, the example pairing classifier 280 determines arelationship threshold to use to classify the media-pairing. Forexample, the pairing classifier 280 may determine whether one or bothmedia in the media-pairing includes a guest appearance by a positiveinfluence guest and/or a negative influence guest, whether one or bothmedia is a one-time event, whether a cross-over between the media isknown, etc.

At block 1312, the example pairing classifier 280 determines whether thepairing score satisfies the relationship threshold. If, at block 1312,the example pairing classifier 280 determined that the pairing scoresatisfies the relationship threshold (e.g., meets and/or exceeds aminimum intersection percentage), then, at block 1314, the examplepairing classifier 280 classifies the media-pairing as related media.If, at block 1312, the example pairing classifier 280 determined thatthe relationship threshold is not satisfied, then, at block 1316, thepairing classifier 280 classifies the media-pairing as not relatedmedia. In the illustrated example, the pairing classifier 280 stores themedia-pairing classification in the example media-pairings database 285of the central facility 110.

After the example pairing classifier 280 classifies the media-pairing asrelated media at block 1314, or after the pairing classifier 280classifies the media-pairing as not related media at block 1316, then,at block 1318, the pairing classifier 280 determines whether anothermedia-pairing for processing exists. If, at block 1318, the pairingclassifier 280 determined that another media-pairing for processingexists, then control returns to block 1302 and the example pairingclassifier 280 awaits another media-pairing to identify. Otherwise, if,at block 1318, the pairing classifier 280 determined that anothermedia-pairing to classify does not exist, the example process 1300 ofFIG. 13 ends.

FIG. 14 is a block diagram of an example processor platform 1400 capableof executing the instructions of FIGS. 8 and/or 9 to implement thesocial media server 105 of FIGS. 1 and/or 2. The processor platform 1400can be, for example, a server, a personal computer, or any other type ofcomputing device.

The processor platform 1400 of the illustrated example includes aprocessor 1412. The processor 1412 of the illustrated example ishardware. For example, the processor 1412 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 1412 of the illustrated example includes a local memory1413 (e.g., a cache). The processor 1412 of the illustrated exampleexecutes the instructions to implement the example social messageinterface 205, the example messages identifier 220 and the example queryhandler 225. The processor 1412 of the illustrated example is incommunication with a main memory including a volatile memory 1414 and anon-volatile memory 1416 via a bus 1418. The volatile memory 1414 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 1416 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 1414, 1416 iscontrolled by a memory controller.

The processor platform 1400 of the illustrated example also includes aninterface circuit 1420. The interface circuit 1420 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1422 are connectedto the interface circuit 1420. The input device(s) 1422 permit(s) a userto enter data and commands into the processor 1412. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1424 are also connected to the interfacecircuit 1420 of the illustrated example. The output devices 1424 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 1420 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 1420 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1426 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1400 of the illustrated example also includes oneor more mass storage devices 1428 for storing software and/or data.Examples of such mass storage devices 1428 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives. The example massstorage 1428 implements the example social message database 210 and theexample social message exposure database 215.

The coded instructions 1432 of FIGS. 8 and/or 9 may be stored in themass storage device 1428, in the volatile memory 1414, in thenon-volatile memory 1416, and/or on a removable tangible computerreadable storage medium such as a CD or DVD.

FIG. 15 is a block diagram of an example processor platform 1500 capableof executing the instructions of FIGS. 10-12 and/or 13 to implement thecentral facility 110 of FIGS. 1 and/or 2. The processor platform 1500can be, for example, a server, a personal computer, or any other type ofcomputing device.

The processor platform 1500 of the illustrated example includes aprocessor 1512. The processor 1512 of the illustrated example ishardware. For example, the processor 1512 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 1512 of the illustrated example includes a local memory1513 (e.g., a cache). The processor 1512 of the illustrated exampleexecutes the instructions to implement the example query engine 255, theexample message logger 260, the example audience estimator 270, theexample pairing classifier 280 and the example reporter 290. Theprocessor 1512 of the illustrated example is in communication with amain memory including a volatile memory 1514 and a non-volatile memory1516 via a bus 1518. The volatile memory 1514 may be implemented bySynchronous Dynamic Random Access Memory (SDRAM), Dynamic Random AccessMemory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or anyother type of random access memory device. The non-volatile memory 1516may be implemented by flash memory and/or any other desired type ofmemory device. Access to the main memory 1514, 1516 is controlled by amemory controller.

The processor platform 1500 of the illustrated example also includes aninterface circuit 1520. The interface circuit 1520 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1522 are connectedto the interface circuit 1520. The input device(s) 1522 permit(s) a userto enter data and commands into the processor 1512. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1524 are also connected to the interfacecircuit 1520 of the illustrated example. The output devices 1524 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 1520 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 1520 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1526 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1500 of the illustrated example also includes oneor more mass storage devices 1528 for storing software and/or data.Examples of such mass storage devices 1528 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives. The example massstorage 1528 implements the example media profile database 250, theexample media messages database 265, the example audience database 275and the example media-pairings database 285.

The coded instructions 1532 of FIGS. 10-12 and/or 13 may be stored inthe mass storage device 1528, in the volatile memory 1514, in thenon-volatile memory 1516, and/or on a removable tangible computerreadable storage medium such as a CD or DVD.

From the foregoing, it will appreciate that the above disclosed methods,apparatus and articles of manufacture identify co-relationships betweenmedia using social media. For example, examples disclosed hereinidentify relationships between television programs by identifyinginstances where a consumer posts social media messages about twodifferent television programs. In some disclosed examples, a totalpercentage of intersection between authors of social media messagesabout the respective television programs is calculated. For example, aconsumer may post a social media message about a televised airing of agolf tournament and then four days later post a social media messageabout an episode of a court-related program. In some disclosed examples,the relationship identified by the social media messages is used toadjust (e.g., optimize) advertising strategies such that similaradvertisements are shown during airings of the related televisionprograms.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus comprising: an audience estimatorto: estimate a first audience of first media based on a first set ofmedia-exposure social media messages corresponding to client devicesreferencing the first media; and estimate a second audience of secondmedia based on a second set of media-exposure social media messagescorresponding to the client devices referencing the second media; and apairing classifier to: determine a pairing-score for a media-pairingbased on the first and second audiences and the first and second sets ofmedia-exposure social media messages; determine a relationship thresholdto apply to the media-pairing based on the first media and the secondmedia; and classify the media-pairing based on the pairing-score and therelationship threshold to improve an accuracy of a system associatedwith generating audience analysis information.
 2. The apparatus asdefined in claim 1, further including a message logger to: obtain themedia-exposure social media messages corresponding to the client devicesby querying a social media server based on media keywords and mediaposting timestamps; and identify the media-pairing including the firstmedia and the second media.
 3. The apparatus as defined in claim 1,wherein the audience estimator is to estimate the first audience by:associating each of the media-exposure social media messages included inthe first set with an author username; and recording a first occurrenceof each of the identified author usernames.
 4. The apparatus as definedin claim 1, wherein the audience estimator is to estimate the firstaudience by: identifying an impression associated with each of themedia-exposure social media messages included in the first set;identifying a user identifier associated with the impression; andrecording a first occurrence of each of the identified impression useridentifiers.
 5. The apparatus as defined in claim 1, wherein theaudience estimator is to estimate the first audience by: identifying afirst subset of usernames associated with posting the first set ofmedia-exposure social media messages to the social media server;identifying a second subset of usernames associated with impressions ofthe media-exposure social media messages included in the first set, eachof the usernames included in the second subset of usernames not presentin the first subset of usernames.
 6. The apparatus as defined in claim1, wherein the pairing classifier is to determine the pairing-score forthe media-pairing by: identifying a subset of audience members includedin the first audience and the second audience; and determining a size ofthe subset of audience members and a first count of media-exposuresocial media messages included in the first set.
 7. The apparatus asdefined in claim 1, wherein the pairing classifier is to classify themedia-pairing by: applying the relationship threshold to thepairing-score; and classifying the first media related to the secondmedia when the pairing-score satisfies the relationship threshold.
 8. Anon-transitory computer readable medium comprising instructions that,when executed, cause a machine to at least: estimate a first audience offirst media based on a first set of media-exposure social media messagescorresponding to client devices referencing the first media; estimate asecond audience of second media based on a second set of media-exposuresocial media messages corresponding to the client devices referencingthe second media; determine a pairing-score for a media-pairing based onthe first and second audiences and the first and second sets ofmedia-exposure social media messages; determine a relationship thresholdto apply to the media-pairing based on the first media and the secondmedia; and classify the media-pairing based on the pairing-score and therelationship threshold to improve an accuracy of a system associatedwith generating audience analysis information.
 9. The non-transitorycomputer readable medium as defined in claim 8, wherein the instructionsare to further cause the machine to: obtain the media-exposure socialmedia messages corresponding to the client devices by querying a socialmedia server based on media keywords and media posting timestamps; andidentify the media-pairing including the first media and the secondmedia.
 10. The non-transitory computer readable medium as defined inclaim 8, wherein the instructions are to further cause the machine to:associate each of the media-exposure social media messages included inthe first set with an author username; and estimating the first audienceby recording a first occurrence of each of the identified authorusernames.
 11. The non-transitory computer readable medium as defined inclaim 8, wherein the instructions are to further cause the machine to:identify an impression associated with each of the media-exposure socialmedia messages included in the first set; identify a user identifierassociated with the impression; and estimate the first audience byrecording a first occurrence of each of the identified impression useridentifiers.
 12. The non-transitory computer readable medium as definedin claim 8, wherein the instructions are to further cause the machineto: identify a first subset of usernames associated with posting thefirst set of media-exposure social media messages to the social mediaserver; and estimate the first audience by identifying a second subsetof usernames associated with impressions of the media-exposure socialmedia messages included in the first set, each of the usernames includedin the second subset of usernames not present in the first subset ofusernames.
 13. The non-transitory computer readable medium as defined inclaim 8, wherein the instructions are to further cause the machine to:identify a subset of audience members included in the first audience andthe second audience; and determine the pairing-score for themedia-pairing by determining a size of the subset of audience membersand a first count of media-exposure social media messages included inthe first set.
 14. The non-transitory computer readable medium asdefined in claim 8, wherein the instructions are to further cause themachine to: apply the relationship threshold to the pairing-score; andclassify the first media related to the second media when thepairing-score satisfies the relationship threshold.
 15. A methodcomprising: estimating, by executing an instruction with a processor, afirst audience of first media based on a first set of media-exposuresocial media messages corresponding to client devices referencing thefirst media; estimating, by executing an instruction with the processor,a second audience of second media based on a second set ofmedia-exposure social media messages corresponding to the client devicesreferencing the second media; determining, by executing an instructionwith the processor, a pairing-score for a media-pairing based on thefirst and second audiences and the first and second sets ofmedia-exposure social media messages; determining, by executing aninstruction with the processor, a relationship threshold to apply to themedia-pairing based on the first media and the second media; andclassifying, by executing an instruction with the processor, themedia-pairing based on the pairing-score and the relationship thresholdto improve an accuracy of a system associated with generating audienceanalysis information.
 16. The method as defined in claim 15, furtherincluding: obtaining the media-exposure social media messagescorresponding to the client devices by querying a social media serverbased on media keywords and media posting timestamps; and identifyingthe media-pairing including the first media and the second media. 17.The method as defined in claim 15, wherein the estimating of the firstaudience includes: associating each of the media-exposure social mediamessages included in the first set with an author username; andrecording a first occurrence of each of the identified author usernames.18. The method as defined in claim 15, wherein the estimating of thefirst audience includes: identifying an impression associated with eachof the media-exposure social media messages included in the first set;identifying a user identifier associated with the impression; andrecording a first occurrence of each of the identified impression useridentifiers.
 19. The method as defined in claim 15, wherein theestimating of the first audience includes: identifying a first subset ofusernames associated with posting the first set of media-exposure socialmedia messages to the social media server; identifying a second subsetof usernames associated with impressions of the media-exposure socialmedia messages included in the first set, wherein each of the usernamesincluded in the second subset of usernames is not present in the firstsubset of usernames.
 20. The method as defined in claim 15, wherein thedetermining of the pairing-score for the media-pairing includes:identifying a subset of audience members included in the first audienceand the second audience; and determining a size of the subset ofaudience members and a first count of media-exposure social mediamessages included in the first set.